
@ARTICLE{MACH2010,
  author = {Yun Li and Cristen J Willer and Jun Ding and Paul Scheet and Gonçalo
	R Abecasis},
  title = {{MaCH}: using sequence and genotype data to estimate haplotypes and
	unobserved genotypes.},
  journal = {Genet Epidemiol},
  year = {2010},
  volume = {34},
  pages = {816--834},
  number = {8},
  month = {Dec},
  abstract = {Genome-wide association studies (GWAS) can identify common alleles
	that contribute to complex disease susceptibility. Despite the large
	number of SNPs assessed in each study, the effects of most common
	SNPs must be evaluated indirectly using either genotyped markers
	or haplotypes thereof as proxies. We have previously implemented
	a computationally efficient Markov Chain framework for genotype imputation
	and haplotyping in the freely available MaCH software package. The
	approach describes sampled chromosomes as mosaics of each other and
	uses available genotype and shotgun sequence data to estimate unobserved
	genotypes and haplotypes, together with useful measures of the quality
	of these estimates. Our approach is already widely used to facilitate
	comparison of results across studies as well as meta-analyses of
	GWAS. Here, we use simulations and experimental genotypes to evaluate
	its accuracy and utility, considering choices of genotyping panels,
	reference panel configurations, and designs where genotyping is replaced
	with shotgun sequencing. Importantly, we show that genotype imputation
	not only facilitates cross study analyses but also increases power
	of genetic association studies. We show that genotype imputation
	of common variants using HapMap haplotypes as a reference is very
	accurate using either genome-wide SNP data or smaller amounts of
	data typical in fine-mapping studies. Furthermore, we show the approach
	is applicable in a variety of populations. Finally, we illustrate
	how association analyses of unobserved variants will benefit from
	ongoing advances such as larger HapMap reference panels and whole
	genome shotgun sequencing technologies.},
  doi = {10.1002/gepi.20533},
  institution = {Department of Genetics, Department of Biostatistics, University of
	North Carolina, Chapel Hill, North Carolina, USA.},
  language = {eng},
  medline-pst = {ppublish},
  owner = {owzar001},
  pmid = {21058334},
  timestamp = {2011.01.18},
  url = {http://dx.doi.org/10.1002/gepi.20533}
}


@ARTICLE{MACH2009,
  author = {Yun Li and Cristen Willer and Serena Sanna and Gonçalo Abecasis},
  title = {Genotype imputation.},
  journal = {Annu Rev Genomics Hum Genet},
  year = {2009},
  volume = {10},
  pages = {387--406},
  abstract = {Genotype imputation is now an essential tool in the analysis of genome-wide
	association scans. This technique allows geneticists to accurately
	evaluate the evidence for association at genetic markers that are
	not directly genotyped. Genotype imputation is particularly useful
	for combining results across studies that rely on different genotyping
	platforms but also increases the power of individual scans. Here,
	we review the history and theoretical underpinnings of the technique.
	To illustrate performance of the approach, we summarize results from
	several gene mapping studies. Finally, we preview the role of genotype
	imputation in an era when whole genome resequencing is becoming increasingly
	common.},
  doi = {10.1146/annurev.genom.9.081307.164242},
  institution = {Center for Statistical Genetics, Department of Biostatistics, University
	of Michigan, Ann Arbor, Michigan 48109-2029, USA. ylwtx@umich.edu},
  keywords = {Base Sequence; Genome, Human; Genotype; Humans; Pedigree; Sequence
	Alignment; Sequence Analysis, DNA},
  language = {eng},
  medline-pst = {ppublish},
  owner = {owzar001},
  pmid = {19715440},
  timestamp = {2011.01.18},
  url = {http://dx.doi.org/10.1146/annurev.genom.9.081307.164242}
}
@ARTICLE{IMPUTE2007,
  author = {Jonathan Marchini and Bryan Howie and Simon Myers and Gil McVean
	and Peter Donnelly},
  title = {A new multipoint method for genome-wide association studies by imputation
	of genotypes.},
  journal = {Nat Genet},
  year = {2007},
  volume = {39},
  pages = {906--913},
  number = {7},
  month = {Jul},
  abstract = {Genome-wide association studies are set to become the method of choice
	for uncovering the genetic basis of human diseases. A central challenge
	in this area is the development of powerful multipoint methods that
	can detect causal variants that have not been directly genotyped.
	We propose a coherent analysis framework that treats the problem
	as one involving missing or uncertain genotypes. Central to our approach
	is a model-based imputation method for inferring genotypes at observed
	or unobserved SNPs, leading to improved power over existing methods
	for multipoint association mapping. Using real genome-wide association
	study data, we show that our approach (i) is accurate and well calibrated,
	(ii) provides detailed views of associated regions that facilitate
	follow-up studies and (iii) can be used to validate and correct data
	at genotyped markers. A notable future use of our method will be
	to boost power by combining data from genome-wide scans that use
	different SNP sets.},
  doi = {10.1038/ng2088},
  institution = {Department of Statistics, University of Oxford, 1 South Parks Road,
	Oxford OX1 3TG, UK.},
  keywords = {Case-Control Studies; Genetic Markers; Genetics, Population; Genome,
	Human; Genomics, statistics /&/ numerical data; Genotype; Humans;
	Models, Genetic; Polymorphism, Single Nucleotide},
  language = {eng},
  medline-pst = {ppublish},
  owner = {owzar001},
  pii = {ng2088},
  pmid = {17572673},
  timestamp = {2011.01.18},
  url = {http://dx.doi.org/10.1038/ng2088}
}
@ARTICLE{IMPUTE2009,
  author = {Bryan N Howie and Peter Donnelly and Jonathan Marchini},
  title = {A flexible and accurate genotype imputation method for the next generation
	of genome-wide association studies.},
  journal = {PLoS Genet},
  year = {2009},
  volume = {5},
  pages = {e1000529},
  number = {6},
  month = {Jun},
  abstract = {Genotype imputation methods are now being widely used in the analysis
	of genome-wide association studies. Most imputation analyses to date
	have used the HapMap as a reference dataset, but new reference panels
	(such as controls genotyped on multiple SNP chips and densely typed
	samples from the 1,000 Genomes Project) will soon allow a broader
	range of SNPs to be imputed with higher accuracy, thereby increasing
	power. We describe a genotype imputation method (IMPUTE version 2)
	that is designed to address the challenges presented by these new
	datasets. The main innovation of our approach is a flexible modelling
	framework that increases accuracy and combines information across
	multiple reference panels while remaining computationally feasible.
	We find that IMPUTE v2 attains higher accuracy than other methods
	when the HapMap provides the sole reference panel, but that the size
	of the panel constrains the improvements that can be made. We also
	find that imputation accuracy can be greatly enhanced by expanding
	the reference panel to contain thousands of chromosomes and that
	IMPUTE v2 outperforms other methods in this setting at both rare
	and common SNPs, with overall error rates that are 15\%-20\% lower
	than those of the closest competing method. One particularly challenging
	aspect of next-generation association studies is to integrate information
	across multiple reference panels genotyped on different sets of SNPs;
	we show that our approach to this problem has practical advantages
	over other suggested solutions.},
  doi = {10.1371/journal.pgen.1000529},
  institution = {Department of Statistics, University of Oxford, Oxford, UK.},
  keywords = {Genetics, Population; Genome-Wide Association Study, methods; Genotype;
	Humans; Polymorphism, Single Nucleotide; Software},
  language = {eng},
  medline-pst = {ppublish},
  owner = {owzar001},
  pmid = {19543373},
  timestamp = {2011.01.18},
  url = {http://dx.doi.org/10.1371/journal.pgen.1000529}
}
@ARTICLE{Halperin2009,
  author = {Eran Halperin and Dietrich A Stephan},
  title = {{SNP} imputation in association studies.},
  journal = {Nat Biotechnol},
  year = {2009},
  volume = {27},
  pages = {349--351},
  number = {4},
  month = {Apr},
  doi = {10.1038/nbt0409-349},
  institution = {International Computer Science Institute, Berkeley, CA, USA.},
  keywords = {Chromosome Mapping, methods; Genetic Variation, genetics; Genome-Wide
	Association Study, methods; Models, Genetic; Polymorphism, Single
	Nucleotide, genetics},
  language = {eng},
  medline-pst = {ppublish},
  owner = {owzar001},
  pii = {nbt0409-349},
  pmid = {19352374},
  timestamp = {2011.01.18},
  url = {http://dx.doi.org/10.1038/nbt0409-349}
}
@ARTICLE{Halperin2009a,
  author = {Eran Halperin and Dietrich A Stephan},
  title = {Maximizing power in association studies.},
  journal = {Nat Biotechnol},
  year = {2009},
  volume = {27},
  pages = {255--256},
  number = {3},
  month = {Mar},
  doi = {10.1038/nbt0309-255},
  institution = {International Computer Science Institute, Berkeley, CA 94704, USA.},
  keywords = {Computational Biology; Genetic Predisposition to Disease; Genome,
	Human; Genome-Wide Association Study, methods; Genotype; Haplotypes;
	Humans; Linkage Disequilibrium; Models, Genetic; Polymorphism, Single
	Nucleotide, genetics},
  language = {eng},
  medline-pst = {ppublish},
  owner = {owzar001},
  pii = {nbt0309-255},
  pmid = {19270676},
  timestamp = {2011.01.18},
  url = {http://dx.doi.org/10.1038/nbt0309-255}
}
